Altı eklemli robot kolunun genetik algoritma ve elman ağ uyarlamalı genelleştirilmiş öngörülü kontrolü

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu tez çalışmasında, Model Tabanlı Öngörülü Kontrol (Model Based Predictive Control-MBPC) sınıfına ait olan Genelleştirilmiş Öngörülü Kontrol (Generalized Predictive Control - GPC), Basit Genetik Algoritma uyarlamalı Genelleştirilmiş Öngörülü Kontrol (SGA-GPC), Newton-Raphson uyarlamalı Yapay Sinir Ağlı Genelleştirilmiş Öngörülü Kontrol (Neural Generalized Predictive Control - NGPC) ve Yinelenen Elman Yapay Sinir Ağ uyarlamalı Genelleştirilmiş Öngörülü Kontrol (Recurrent Elman Network implemented Neural Generalized Predictive Control - ENGPC) algoritmaları altı eklemli endüstriyel bir robotik manipülatöre eklem esaslı yörünge kontrolü için uygulanmıştır. Robotik manipülatörün dinamik modellenmesinde Lagrange-Euler yöntemi kullanılmıştır. Dinamik modellemeye sürtünme etkileri, yük taşıma ve taşınan yükün taşıma esnasında düşmesi durumları da ilave edilmiştir. Ayrıca, kontrolü güçleştirmek için ile arasında rasgele bozucular ilave edilmiştir. Dinamik model, 4. mertebeden Runge-Kutta bütünleştirme yöntemi kullanılarak robot kolu simülatörüne dönüştürülmüştür. Tasarlanan kontrol algoritmalarının performansı eklemlere ait tork, açısal yörünge, açısal hız, açısal hız hataları grafikleri ile eklemlere ait açısal konum hataları, açısal hız hatalarının kareleri ve uç nokta konum hataları üzerinden hem grafiksel hem de nümerik sonuçlarla karşılaştırılmıştır.In this thesis study, Generalized Predictive Control (GPC), Neural Generalized Predictive Control (NGPC), Simple Genetic Algorithm implemented GPC (SGA-GPC) and Recurrent Elman Neural Network implemented NGPC (ENGPC) algorithms belong to the class of Model Based Predictive Control (MBPC) were investigated and each of them was applied to a 6-DOF (Degrees-Of-Freedom) robotic manipulator as SISO (Single Input Single Output) and MIMO (Multiple Inputs Multiple Outputs) for the trajectory control based joint. Dynamics modeling of the robotic manipulator was made by using the Lagrange-Euler equations. The frictional effects, the state of carrying and falling load were also added to dynamics model. In addition, the random distortions between and were added to the torques applied to the joints in every control step, and the effect to the performance of the distortions was investigated. Dynamics model was transformed into robotic arm simulator by using the fourth-order Runge-Kutta integration method. The trajectory planning for the joints of the robotic arm was designated according to the sinusoidal trajectories principles. The control algorithms were compared with themselves for different examples and cases

Neural Network Local Navigation of Mobile Robots in a Moving Obstacles Environment

IF AC Intelligent Components and Instruments for Control Applications, Budapest, Hungary, 1994This paper presents a local navigation method based on generalized predictive control. A modified cost function to avoid moving and static obstacles is presented. An Extended Kaiman Filter is proposed to predict the motions of the obstacles. A Neural Network implementation of this method is analysed. Simulation results are shown.Ministerio de Ciencia y Tecnología TAP93-0408Ministerio de Ciencia y Tecnología TAP93-058

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained stochastic optimal control with dynamics learning and feedback control. We derive an iterative convex optimization algorithm that solves an \underline{Info}rmation-cost \underline{S}tochastic \underline{N}onlinear \underline{O}ptimal \underline{C}ontrol problem (Info-SNOC). The optimization objective encodes both optimal performance and exploration for learning, and the safety is incorporated as distributionally robust chance constraints. The dynamics are predicted from a robust regression model that is learned from data. The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints. A stable feedback controller is used to execute the motion plan and collect data for model learning. We prove the safety of rollout from our exploration method and reduction in uncertainty over epochs, thereby guaranteeing the consistency of our learning method. We validate the effectiveness of Info-SNOC by designing and implementing a pool of safe trajectories for a planar robot. We demonstrate that our approach has higher success rate in ensuring safety when compared to a deterministic trajectory optimization approach.Comment: Submitted to RA-L 2020, review-

Non-linear predictive control for manufacturing and robotic applications

The paper discusses predictive control algorithms in the context of applications to robotics and manufacturing systems. Special features of such systems, as compared to traditional process control applications, require that the algorithms are capable of dealing with faster dynamics, more significant unstabilities and more significant contribution of non-linearities to the system performance. The paper presents the general framework for state-space design of predictive algorithms. Linear algorithms are introduced first, then, the attention moves to non-linear systems. Methods of predictive control are presented which are based on the state-dependent state space system description. Those are illustrated on examples of rather difficult mechanical systems

A new T-S fuzzy model predictive control for nonlinear processes

Abstract: In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems